This paper describes a system design and hybrid control algorithm of an electrically actuated lower limb exoskeleton (LLE). The system design mainly includes three parts: mechanical structure design, actuation system design and sensor system design. According to the initial state of the joint angle, LLE can be divided into Non-anthropomorphic state (NAS) and anthropomorphic state (AS). The human motion intention (HMI) estimation can be divided into gait phase classification and reference trajectory estimation. The fuzzy logic is used to detect different phases in the gait phase classification. In the reference trajectory estimation, the kinematic model of the LLE is utilized to obtain a continuous joint trajectory, which is used as input of the control law. To make the LLE accurately follow the movement of people and remain stable, a hybrid dual-mode control strategy is proposed in this paper, i.e., the adaptive impedance control (AIC) method is used to improve the stability and resistance to shock in stance phase, and the active disturbance rejection control with the fast terminal sliding mode control (ADRC-FTSMC) method is employed to improve the response speed and the tracking precision in swing phase. Furthermore, in order to solve the torque discontinuity in the switching process, a smoothing method is proposed during the transition. Finally, the prototype experiments were set up to verify the tracking performance and power-assisted effect of the proposed exoskeleton. The experiments results show the LLE can achieve excellent tracking performance and power-assisted effect based on the proposed HMI methodology and hybrid dual-mode control strategy.

Exoskeletons can track the wearer’s movements in real time, thereby enhancing physical performance or restoring mobility for individuals with gait impairments. These wearable assistive devices have demonstrated significant potential in both rehabilitation and industrial applications. This review focuses on the major advancements in exoskeleton technology published since 2020, with particular emphasis on the development of structural designs for lower-limb exoskeletons employed in locomotion assistance. We employed a systematic literature review methodology, categorizing the included studies into three main types: rigid exoskeleton, soft exoskeleton, and tethered platform. The current development status of robotic exoskeletons is analyzed based on publication year, system weight, target assistive joints, and main effects. Furthermore, we examine the factors driving these advancements and their implications for the field. The key challenges and opportunities that may influence the future development of exoskeleton technologies are also highlighted in this review.

An increasing number of grounded robots are being used in prostate interventions to improve clinical outcomes, but their large size and high-cost limit their popularity. Thus, we present a hand-held 3-degree-of-freedom (DoF) parallel robot with remote center of motion (RCM) for minimally invasive prostate biopsy applications, combining the flexibility of hand-held devices with the precision of robotic assistance. First, the kinematic structure of robotic assistance is introduced according to its design requirements. Then, the kinematic analysis of robotic assistance is carried out by using a simplified kinematic model. The kinematic parameters are designed according to the desired workspace. A prototype has been developed and validated in animal experiments. Twenty beagles of different sizes were selected for the robot-assisted and controlled experiments, resulting in target errors of 3.30 ± 1.63 mm and 5.40 ± 1.76 mm, respectively. The error of robot-assisted experiments was significantly better than in controlled experiments. Preliminary animal tests have demonstrated that the hand-held robot can improve the accuracy of free-hand biopsy punctures.

This paper addresses a detection problem where sparse measurements are utilized to estimate the source parameters in a mixed multi-modal radiation field. As the limitation of dimensional scalability and the unimodal characteristic, most existing algorithms fail to detect the multi-point sources gathered in narrow regions, especially with no prior knowledge about intensity and source number. The proposed Peak Suppressed Particle Filter (PSPF) method utilizes a hybrid scheme of multi-layer particle filter, mean-shift clustering technique and peak suppression correction to solve the major challenges faced by current existing algorithms. Firstly, the algorithm realizes sequential estimation of multi-point sources in a cross-mixed radiation field by using particle filtering and suppressing intensity peak value, while existing algorithms could just identify single point or spatially separated point sources. Secondly, the number of radioactive sources could be determined in a non-parametric manner as the fact that invalid particle swarms would disperse automatically. In contrast, existing algorithms either require prior information or rely on expensive statistic estimation and comparison. Additionally, to improve the prediction stability and convergent performance, distance correction module and configuration maintenance machine are developed to sustain the multimodal prediction stability. Finally, simulations and physical experiments are carried out in aspects such as different noise level, non-parametric property, processing time and large-scale estimation, to validate the effectiveness and robustness of the PSPF algorithm.

Wearable assistive devices (WADs) show potential for future applications in industrial and medical fields. However, wearing discomfort, caused by factors such as additional weight penalties, inappropriate straps, and joint misalignment, may disrupt the natural balance of human motion, leading to increased metabolic expenditure, muscle fatigue, and even physical injury. These challenges severely restrict the practical application of WADs. To address these challenges, we have developed a reactionless, lightweight WAD that utilizes a single device attached to the shoe, eliminating the need for additional rigid links or straps on the shank or thigh. This design provides assistive torque directly to the target limbs during human locomotion. The proposed device weighs 0.67 kg and can provide an assistive torque of 12 N·m and mechanical power of 10 W. Furthermore, we introduced a human-in-the-loop multiobjective optimization approach considering the assistance effectiveness, power consumption, system size, service safety, and actuator constraints to achieve relative optimality in performance of the proposed device. Cardiopulmonary exercise test and surface electromyography test results from seven participants indicated that the device can reduce the average gross metabolic rate by 12.62%, and the muscle activation of the rectus femoris and soleus by 35.12% and 4.10%, respectively.